Various types of heart rhythm disorders are known, some of which are life-threatening and require immediate attention and treatment, such as ventricular fibrillation. Other arrhythmias may require treatment, and/or may be symptomatic of other underlying conditions requiring treatment, but are typically not immediately life-threatening. Atrial fibrillation (“AF”), for example, is a relatively common cardiac arrhythmia associated with increased risk of stroke and death. Other less-common cardiac arrhythmias that would be beneficial to diagnose include, but are not limited to, paroxysmal ventricular tachycardia, paroxysmal atria tachycardia, supraventricular tachycardia, and sinus tachycardia. Although the following disclosure will, for simplicity, refer to AF, it will be understood that the disclosed methods are also generally applicable to other less-common cardiac arrhythmias.
AF is the most common disturbance of the heart rhythm requiring treatment. Epidemiologic data estimates that 2.2 million individuals suffer from AF in the United States. The prevalence of AF is approximately 2-3% in patients older than 40 years of age and 6% in those individuals over 65 years and 9% in individuals over 80 years old. As the U.S. population ages, AF will become more prevalent. It is estimated that over 5 million Americans will suffer from AF by the year 2050. AF is associated with a doubling of mortality rate of people afflicted with AF compared to people who are not, and an increased risk of stroke of about 5% per year.
AF can be either symptomatic or asymptomatic, and can be paroxysmal or persistent. Symptomatic AF is a medical condition wherein symptoms are readily detectable by experts in the field. AF is usually diagnosed when a patient exhibits associated symptoms or complications, such as palpations, congestive heart failure or stroke. AF may also be diagnosed incidentally during a routine medical evaluation.
Patients with asymptomatic paroxysmal AF may be exposed to the risk of devastating consequences such as stroke, congestive heart failure, or tachycardia-mediated cardiomyopathy, for years before a definitive diagnosis of AF can be made.
Current standard techniques and devices for detecting AF include a resting electrocardiogram, which records about 15 seconds of cardiac activity, a Holter monitor, which records 24-48 hours of cardiac activity during routine daily activities, and an event monitor, which only records cardiac activity when the patient activates the monitor because the patient has detected symptoms associated with AF. These diagnostic methods and tools have significant limitations in diagnosing AF and assessing the efficacy of treatment because of the limited recording time windows of these methods and tools.
Moreover, pharmacologic treatment of AF may convert patients with symptomatic AF into patients with asymptomatic AF. In a retrospective study of four studies comparing the drug Azimilide to a placebo where, in the absence of symptoms, routine trans-telephonic electrocardiograms were recorded for 30 seconds every two weeks, asymptomatic AF was present in 17% of the patients. In another study of 110 patients with permanently implanted pacemakers who had a history of AF, the condition was diagnosed in 46% of the patients using electrocardiogram recording and in 88% of the patients using stored electrograms recorded by the implanted pacemaker.
Review of data stored in implanted devices, such as pacemakers, revealed that 38% of AF recurrences lasting greater than 48 hours were completely asymptomatic. Finally, using data obtained from ambulatory monitors used on patients with paroxysmal AF over a 24-hour period, studies show a high frequency of occurrence of asymptomatic AF among patients treated with the drugs propranolol or propafenone. In the above-mentioned study, 22% of the patients on propranolol and 27% of the patients on propafenone were diagnosed with asymptomatic AF.
Under-detection and under-recognition of AF in patients may have significant clinical consequences, including clinical exposure of patients to an increased risk of cardio-embolic stroke before detection of the arrhythmia and initiation of appropriate stroke prevention measures, difficulty of assessment of the efficacy of rhythm control intervention, and overestimation of successful maintenance of sinus rhythm.
However, screening for many dangerous arrhythmias can be problematic. A known challenge in the detection of dangerous arrhythmias from heart activity data such as EKG data is that a generally healthy heart may often exhibit some variability in the EKG data that can confuse or mislead automated detection algorithms. Relatively benign variability may include premature atrial contraction, premature ventricular contraction, and normal sinus arrhythmia.
A common problem with screening for potentially dangerous heart rhythm irregularities, such as atrial fibrillation, ventricular tachycardia, and the like, is that existing detection methods lack sufficient specificity. Existing detection methods produce a significant number of false positives, generating anxiety in healthy subjects, causing expensive technician review, and possibly spurring unnecessary, expensive, potentially uncomfortable, and inconvenient additional testing.
Detection of AF, automatically or manually, based on statistical data, requires the use of thresholds defined with respect to sensitivity and specificity. The thresholds used define the point beyond which a set of data indicates existence of AF. Sensitivity and specificity are defined as follows. In a dichotomous experiment, a given event, e, falls into one of two sets, such as a set of positive events, P, and a set of negative events, N. The set P includes events p and the set N includes events n.
A detection test may be performed to determine that the given event e belongs to the set P or to the set N in a dichotomous experiment. Sensitivity is a measure of how well the detection test can correctly identify the given event e of the set P as belonging to the set P. Such events e that are correctly identified as belonging to the set P are known as true positives (“TP”). Such events e that are misidentified as belonging to the set N are known as false negatives (“FN”).
Sensitivity is defined as the ratio of the number of true positive events detected correctly by the test to the total number of actual positive events p. The total number of actual positive events is equal to the sum of the TP and FN. That is, sensitivity=TP/(TP+FN). A low sensitivity detection test will misidentify more positive events as belonging to the set N than a high sensitivity detection test.
Specificity is the dual of sensitivity and is a measure of how well the detection test can correctly identify the given event e of the set N as belonging to the set N. Such events e that are correctly identified as belonging to the set N are known as true negatives (“TN”). Such events e that are misidentified as belonging to the set P are known as false positives (“FP”). Specificity is defined as the ratio of the number of true negative events detected correctly by the test to the total number of actual negative events n. The total number of actual negative events is equal to the sum of the TN and FP. That is, specificity=TN/(TN+FP). A low specificity detection test will misidentify more negative events as belonging to the set P than a high specificity detection test.
A prominent characteristic of AF is heart rate variability. There have been attempts to use the variability of heart interbeat (“RR”) intervals directly to identify AF, resulting in a sensitivity of 94% and specificity of 97% using a threshold based on the Kolmogorov-Smirnov test. The Kolmogorov-Smirnov test is used to decide if a statistical sample belongs to a population with a specific probability distribution.
Long-term monitoring of cardiac activity is desirable for timely detection of AF, but the storage requirements can be prohibitive. To digitize a single channel EKG at 100 samples per second and 10-bit resolution, which constitute near minimum requirements for a high quality signal, for 90 days of continuous recording requires 927 megabytes of storage. Although providing this amount of storage is possible, it is also costly. Advances in electronics allow the design of portable devices that can pre-process and classify the signals to avoid storage of normal rhythms and save the storage capacity for recording of abnormal rhythms, for example, rhythms indicating episodes of atrial fibrillation.
A device is desired for the long-term monitoring of a subjects heart rhythm that is inexpensive, non-invasive, highly accurate, and convenient for the patient. These requirements at least indicate that the monitoring device should be light and small. As such, a device is desired with low power requirements and with a significant amount of storage. The storage capacity may possibly be extended by using an algorithm for the elimination of EKG data that indicate very low-probability of AF or other target arrhythmia. This algorithm should be small in size and simple in operation to reduce processing power needs and electrical power requirements.
For response to and treatment of ventricular tachycardia and ventricular fibrillation in real-time, often an implantable cardiac defibrillator (“ICD”) is surgically implanted in the patient and coupled with the heart to monitor heart rhythm and detect these life-threatening rhythm disturbances. The ICD typically includes logic components implemented in software/firmware and/or hardware for detecting arrhythmia. Once life-threatening arrhythmia is detected, the ICD logic component may, based on a discrimination algorithm, determine that some action, such as administering an electric shock (defibrillation), must be taken to treat the arrhythmia.
However, this determination can be erroneous. In some ICDs such inappropriate shocks can occur in 15% of all patients within a 46-month follow-up. Certain sub-populations may have a higher rate of inappropriate shocks, for example, this can occur in as many as 38% of younger patients. A common cause of inappropriate shock in these patients is AF, although virtually any supraventricular tachycardia can cause an inappropriate shock.
When not needed, an electric shock causes extreme discomfort and/or pain to the patient and may be potentially dangerous. Accordingly, a more accurate discrimination algorithm is needed to discriminate between cases where an electric shock is needed and cases where an electric shock is not needed. One approach that has been used is based on the heart rate, or beat-to-beat intervals. Another approach uses the morphology of the electrocardiographic complexes to help discriminate, with a representative recent method being the use of wavelet-transforms. In spite of these methods, inappropriate shocks continue to be a problem in such devices.